To tackle the first challenge, we treat the human-in-the-loop recognition and updating procedure as sequential interactions between the human expert and the identity recognition agent. To simplify the problem, we mainly consider vector-form streaming data such as gait information. A tree-structured model [1]} is deployed to be the base recognition model due to its explainability and scalability. To address the second and third challenges, a reinforcement learning (RL) based approach is leveraged to automatically learn the model updating strategies according to the human feedback. In the Markov Decision Process (MDP) [2]}, an action can be determined by an RL algorithm based on the state and reward, which is similar to the updating strategy selection procedure based on the interaction between identification agent and human expert. Therefore, RL is appropriate to select optimal model updating strategies by reasonably combining the current identification model performance and human efforts.
